Trend-following trading strategies in commodity futures: A re-examination

Szakmary, Andrew C.; Shen, Qian; Sharma, Subhash C.

doi:10.1016/j.jbankfin.2009.08.004

Cited by 196 publications

(126 citation statements)

References 38 publications

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“…The results from this study are consistent with those of Brock et al (1992), Gencay (1998), Lukac et al (1988), Park and Irwin (2009) and Szakmary et al (2010) supporting the hypothesized ability of most technical indicators to make excessive return higher than the buy and hold strategy, even after transaction costs are taken into account (Brock et al, 1992, Park andIrwin, 2009). …”

Section: Resultssupporting

confidence: 85%

Returns from neural network enhanced technical analysis indicator: A study on crude oil futures

M’ng¹,

Aziz²,

Ismail

2017

Int. j. adv. appl. sci

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Section: Resultssupporting

confidence: 85%

Returns from neural network enhanced technical analysis indicator: A study on crude oil futures

M’ng¹,

Aziz²,

Ismail

2017

Int. j. adv. appl. sci

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“…Both Jegadish and Titman (2001) and Conrad and Kaul (1998) Asness, Moskowitz andPedersen (2009), andMoskowitz, Ooi and. However, both Korajczyk and Sadka (2004) and Lesmond, Schill and Zhou (2004) suggest that once transactions' costs are fully incorporated into these momentum-based trading rules, especially the cost of short-selling, then the abnormal profits that appear to be available to the equity strategies disappear, though the finding that abnormal profits persist for commodity futures where transactions' costs are much lower suggest that momentum profits may be more pervasive elsewhere (see for example Szakmary, Shen, Sharma (2010) and Miffre and Ralis (2007)). …”

Section: Trend Following and Momentum Strategiesmentioning

confidence: 99%

“…In summary then, although many studies examine exhaustively a variety of trading rules, especially of late those applied to commodity futures (see Szakmary et al (2010) iv) trend following rules give superior risk-adjusted returns relative to using fundamental financial metrics…”

Section: Trend Following and Momentum Strategiesmentioning

confidence: 99%

Breaking into the blackbox: Trend following, stop losses and the frequency of trading – The case of the S&P500

Clare¹,

Seaton²,

Smith³

et al. 2013

J Asset Manag

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This is the accepted version of the paper.This version of the publication may differ from the final published version. In this paper we compare a variety of technical trading rules in the context of investing in the S&P500 index. These rules are increasingly popular both among retail investors and CTAs and similar investment funds. We find that a range of fairly simple rules, including the popular 200-day moving average trading rule, dominate the long only, passive investment in the index. In particular, using the latter rule we find that popular stop loss rules do not add value and that monthly end of month investment decision rules are superior to those which trade more Permanent repository linkElectronic copy available at: http://ssrn.com/abstract=2126476 2 frequently: this adds to the growing view that trading can damage your wealth. Finally we compare the MA rule with a variety of simple fundamental metrics and find the latter far inferior to the technical rules over the last 60 years of investing.

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“…Moreover, traditional wisdom views oil prices as exceptionally informative with respect to future economic activities and volatilities. In addition, oil production, oil inventories and real activity are also found to provide valuable information about oil prices and volatilities (e.g., Hong and Yogo, 2012;Szakmary et al 2010). Oil shocks, based on the indicators mentioned herein, can summarize the information of oil markets and may better capture oil volatilities.…”

Section: Discussionmentioning

confidence: 96%

What Drives Long-term Oil Market Volatility? Fundamentals versus Speculation

Yin

Zhou

2016

Economics

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This paper explores the role of speculation and economy fundamentals in the oil market using a two-component GARCH-MIDAS model. Specifically, the authors highlight the different roles played by the changing oil shocks with respect to the short-term and long-term components regarding oil market volatility. The results indicate that a global demand shock is the only factor found not only to be positive but to also significantly increase long-and short-term oil volatility in the full sample. This is consistent with a classic host of research that advocates that global demand dominates the oil market. However, since 2004, impacts of other oil shocks have been significantly weakened or even reversed. For example, the speculative demand shock has helped to stabilize long-term oil volatility during the post-2004 period. The results also suggest the existence of asymmetric impacts on short-term oil volatility, particularly for shocks from oil supply, oil-specific demand and oil speculative demand. JEL Q43

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Trend-following trading strategies in commodity futures: A re-examination

Cited by 196 publications

References 38 publications

Returns from neural network enhanced technical analysis indicator: A study on crude oil futures

Returns from neural network enhanced technical analysis indicator: A study on crude oil futures

Breaking into the blackbox: Trend following, stop losses and the frequency of trading – The case of the S&P500

What Drives Long-term Oil Market Volatility? Fundamentals versus Speculation

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